Q1: What do the values mean under each column ? What is PC1? Is it the equivalent of Column1 of my dataset (i.e the first feature/variable)?
First, it helps to have a conceptualization of what a prinicipal component is.
Let's say your customers have 6 charasteristics:
height, weight, friendliness (rated by observers), number of friends, income, years of schooling
It is obvious that these characteristics have commonalities in pairs: 1-2, 3-4, 5-6. Mathematically this means that a tall person is also heavier and has higher values in both characteristics and so forth for the other pairs. In other words, even though you have 6 characteristics, these can be abstrtacted in 3 supercharacteristics: body size, extroversion, Socioeconomic status. These 3 abstractions are your significant (as identified by variance explained, elbow method) principal components, PC1, PC2 and PC3 (the ranking is based on which explains the most variance).
PC1 is the abstracted concept that generates (or accounts for) the most variability in your data. PC2 for the second most variability and so forth
The value under the column represents where the individual stands (z-score) on the distribution of the abstracted concept, e.g. someone tall and heavy would have a +2 z-score on PC1 (body size).
Q2: What do these clusters represent? What are the characteristics that been used to properly segment them ? What is the x and y axes represent? How can I use the final cluster result concretely by applying it with new data, for example: New customer X is part of cluster 2(e.g. Valuable customer) based on this and that data.
The clusters represent a tendency of some of your data points (customers) to have commonalities and thus clump together in Euclidean space. For instance, in our example assume in your customers you have programmers and business people. Former would form a cloud high in income and low in extroversion (stereotype, I know), latter would form a cloud high income high extraversion.
The characteristics for segmentation are the ones you provided in your code. I am assuming you are following code from a book and therefore they should be the significant principal components. But it is impossible to know without seeing your code.
X and Y values are again impossible to know without seeing your code. Assuming you are using code from a book, they should be the first 2 principal components.
Generally the principal Components are reliable for this analysis, otherwise you run the risk of weighting some characteristics more heavily than others.
To concretely use this result, you want first of all to compare your 3 clusters for the average revenue they bring to you. You need an ANOVA comparing the 3 groups: revenue ~ cluster.
Then you need to build a Supervised Learning model (for instance, k-nearest neighbor) which takes a new data point and assigns it to one of the clusters, based on its characteristics. If it turns out that one of the clusters yields more average revenue and the new customer is assigned by the model to this cluster, then this is a potentially valuable customer.
This is all high-level, but I hope it contributes to your understanding.